Risk Classification in Nonlife Insurance Premium Ratemaking

The use of data mining techniques is not new—commonly it is used in various other industries, such as financial services, marketing and manufacturing. The main goal of data mining is to find patterns in a large dataset that yield insight and expertise. Thus, in terms of healthcare, data mining methods have a wide range of uses, including diagnosing cancers, pattern recognition and prognosti-cating patient health outcomes. Each patient's diagnosis at the University of Porto Hospital (Centro Hospitalar Universitário Universitário do Porto) has an ICD-10-CM code. This data can be used to build a predictive model to classify diag-nosis using secondary diagnosis. Three datasets were then created to be tested using data mining techniques. As a result, the algorithm that had the best perfor-mance was the Random Tree (99.8% corrected classified instances) using the third dataset with the five main diagnoses of each patient as parameters

Recent advances in computing, communications, digital storage technologies, and high-throughput data-acquisition technologies, make it possible to gather and store incredible volumes of data. It creates unprecedented opportunities for large-scale knowledge discovery from huge database. Data mining (DM) technology has emerged as a means of performing this discovery. There are countless researchers working on designing efficient data mining techniques, methods, and algorithms. Many data mining methods and algorithms have been developed and applied in a lot of application fields [1]. Unfortunately, most data mining researchers pay much attention to technique problems for developing data mining models and methods, while little to basic issues of data mining [2]. In this talk, some basic issues of data mining are addressed. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? What is the rule we should obey in a data mining process? Through analyzing existing data mining methods, and domain-driven (or user-driven) data mining models [3-5], we find that we should take a data mining process as a process of knowledge transformation. Based on this understanding of data mining, a conceptual data mining model of domain-oriented data-driven data mining (3DM) is proposed [2]. The relationship between traditional domain-driven (or user-driven) data mining models and our proposed 3DM model is also analyzed. Some domain-oriented data-driven data mining algorithms for mining such knowledge as default rule [6], decision tree [7], and concept lattice [8] from database are proposed. The experiment results for these algorithms are also shown to illustrate the efficiency and performance of the knowledge acquired by our 3DM data mining algorithms.

Predictive analytics has emerged as a powerful tool in the field of financial engineering, enabling organizations to forecast trends, optimize strategies, and mitigate risks. Data mining techniques play a crucial role in extracting meaningful patterns from large volumes of financial datasets, contributing to the development of predictive modeling techniques. The purpose of this paper is to explore the application of various data mining techniques in predictive analytics within the financial sector, with a focus on their effectiveness in risk management, fraud detection, and portfolio optimization. Machine learning algorithms, such as decision trees, support vector machines, and neural networks, are increasingly being employed to model complex financial systems and enhance predictive accuracy. Additionally, techniques like clustering, association rule mining, and time series analysis are used to uncover hidden relationships and temporal patterns in financial datasets. Despite the significant advancements in these techniques, challenges remain in terms of data quality, computational complexity, and interpretability of results. This paper addresses these challenges and provides a comprehensive review of the most widely used data mining methods in financial engineering. Furthermore, it discusses the potential for future advancements in data mining algorithms and their integration with other emerging technologies like artificial intelligence and big data analytics. The findings indicate that while data mining offers significant potential in financial analytics, careful consideration must be given to the selection of appropriate techniques and the proper handling of data to ensure reliable predictions. Ultimately, this paper aims to contribute to the ongoing development of robust predictive modeling techniques in financial engineering by emphasizing the importance of data mining as an indispensable tool.

The way we learn has changed as a result of advances in science and technology and the growing influence of information technology on daily life. E-learning is a relatively new concept in educational settings, but it has progressively earned a spot in modern training techniques. Data mining methods are used in many applications, particularly in elearning. Comparing direct communications difficult to track the student involvement in online courses. To overcome the above mentioned issues the existing methods are analyzed various techniques of data mining and optimization methods which can be applied to bring out hidden knowledge from the educational data? In the e-learning work, preprocessing, feature selection and classification process are performed to improve the Kalboard 360 educational dataset accuracy significantly. This study focused on educational data mining (EDM) and employed decision trees, support vector machines (SVM), multilayer perceptrons, and the naive bayesian C4.5 algorithm among other classification techniques to assess the features for the prediction of students’ behaviour and academic achievement. This paper analyzes the advantages and shortcomings of each algorithm applied to data set on the basis of accuracy for various classification techniques like Multilayer Perceptron based ANN algorithm (MP-ANN), Decision Tree (DT) C4.5 and Hybrid Regression model with Multi Label Classification (HR-MLC) approach. The experimental result shows that the HR-MLC algorithm provides better performance in terms of higher accuracy, precision, recall and f-measure rather than the other existing methods.

Discover the next generation of data-mining tools and technologyThis book brings together an international team of eighty experts to present readers with the next generation of data-mining applications. Unlike other publications that take a strictly academic and theoretical approach, this book features authors who have successfully developed data-mining solutions for a variety of customer types. Presenting their state-of-the-art methodologies and techniques, the authors show readers how they can analyze enormous quantities of data and make new discoveries by connecting key pieces of data that may be spread across several different databases and file servers. The latest data-mining techniques that will revolutionize research across a wide variety of fields including business, science, healthcare, and industry are all presented. Organized by application, the twenty-five chapters cover applications in:. Industry and business . Science and engineering . Bioinformatics and biotechnology . Medicine and pharmaceuticals. Web and text-mining . Security. New trends in data-mining technology. And much more . . .Readers from a variety of disciplines will learn how the next generation of data-mining applications can radically enhance their ability to analyze data and open the doors to new opportunities. Readers will discover:. New data-mining tools to automate the evaluation and qualification of sales opportunities. The latest tools needed for gene mapping and proteomic data analysis. Sophisticated techniques that can be engaged in crime fighting and preventionWith its coverage of the most advanced applications, Next Generation of Data-Mining Applications is essential reading for all researchers working in data mining or who are tasked with making sense of an ever-growing quantity of data. The publication also serves as an excellent textbook for upper-level undergraduate and graduate courses in computer science, information management, and statistics

Data Mining refers to a set of methods applicable to large and complex databases to eliminate the randomness and discover the hidden pattern. Datamining (DM), also known as knowledge discovery from databases (KDD), is the extraction of new knowledge from huge databases. Data mining involves the use of sophisticated data analysis tools to discover previously unknown, valid patterns and relationships in large data sets. Data mining tools can forecast the future trends and activities to support the decision of people. The scope of datamining is associated with Uncovering trends and patterns are a great power for the businesses of all sectors and industries. Modern intrusion detection applications are confronted with a variety of issues. These applications must be reliable, extensible, manageable, and minimal in maintenance costs. Data mining-based intrusion detection systems (IDSs) have shown high accuracy, good generalisation to novel types of intrusion, and stable behaviour in a changing environment in recent years. The number of hidden layers in various neural network topologies is evaluated in order to discover the best neural network. The technique of attempting to discover instances of network attacks by comparing current behaviour to the expected actions of an intruder is known as misuse detection. Artificial neural networks have the ability to detect and classify network activity using data that is limited, incomplete, and nonlinear. The main purpose of this work is to identify privacy and security concerns among cloud computing participants and consumers in a distributed environment. Techniques like Machine Learning, Natural Language Processing (NLP), and Data Mining are combined to automatically identify and uncover patterns from many sorts of materials. Predictive analytics is capable of dealing with both continuous and discontinuous changes. Classification, prediction, and to some extent, affinity analysis constitute the analytical methods employed in predictive analytics. The contemporary study in text or document mining is focusing on syntactic components and the semantic environment. In order to accomplish this, and with the motivation gained from our previous research contributions, we investigated a mining model to classify documents based on the Order of Context, Concept, and Semantic Relations (OCCSR). The use of data mining techniques based on Cloud computing will enable users to retrieve meaningful information from virtually integrated data warehouses, lowering infrastructure and storage costs. Data mining can extract useful and potentially useful information from the cloud. Big Data is typically defined by three characteristics known as the 3Vs (Volume, Velocity and Variety). The surveys approaches, environments, and technologies in key areas for Big Data analytics capabilities and discusses how they aid in the development of analytics solutions for Clouds. The clustering technique belongs to an unsupervised learning and it is used to discover a new set of categories. Grid-based clustering has the shortest processing time, which is typically determined by the size of the grid rather than the data. We compare the performance of three clustering algorithms: hierarchical clustering, density-based clustering, and K Means clustering. The majority of current approaches to detecting misuse involve the use of rule-based expert systems to identify indicators of known attacks. We provide a brief overview of the use of various Artificial Intelligence techniques and their advancements in the design, development, and application of Intrusion Detection Systems (IDS) for protecting computer and communication networks from intruders. The goal of Knowledge Discovery in Data (KDD) is to extract information that is not obvious by using careful and detailed analysis and interpretation. To drive decisions and actions, analytics employs KDD, data mining, text mining, statistical and quantitative analysis, explanatory and predictive models, and advanced and interactive visualisation techniques.

Probabilistic neural networks are applied to facilitate slope stability estimation by making use of data mining techniques and an intelligent system. Data mining can draw attention to meaningful structures in the archives of such slope stability data. The soil mechanical characteristics and the slope shapes with significant influences on slope stability are used to train and test the neural networks. Validation is performed to show the efficiency of probabilistic neural networks for estimation of slope stability. The simulation results show that probabilistic neural network models generate higher predicting precision than the conventional linear regression, limit equilibrium method and maximum likelihood estimation.

One of the most difficult tasks in the whole KDD process is to choose the right data mining technique, as the commercial software tools provide more and more possibilities together and the decision requires more and more expertise on the methodological point of view. Indeed, there are a lot of data mining techniques available for an environmental scientist wishing to discover some model from her/his data. This diversity can cause some troubles to the scientist who often have not a clear idea of what are the available methods, and moreover, use to have doubts about the most suitable method to be applied to solve a concrete domain problem. Within the data mining literature there is not a common terminology. A classification of the data mining methods would greatly simplify the understanding of the whole space of available methods. In this work, a classification of most common data mining methods is presented in a conceptual map which makes easier the selection process. Also an intelligent data mining assistant is presented. It is oriented to provide model/algorithm selection support, suggesting the user the most suitable data mining techniques for a given problem.

According to the World Health Organization (WHO), it has been recorded that up to now more than 150 million people have diabetes, whether they are elderly people, adults, teenagers, men or women. Early knowledge of diabetes can be seen based on data from patients who already have diabetes. The patient's disease data has previously been stored and arranged in a data warehouse or what is commonly referred to as a dataset. Therefore, it is necessary to process the data contained in the dataset. But the use of data mining techniques themselves must be assisted by using the techniques contained in the data mining, namely classification techniques. K-Nearest Neighbor (K-NN) is one of the methods used in the classification technique. In the results of the classification of the level of confidence obtained in the process, it is seen based on the amount of accuracy. However, there are important issues that need special attention. In the dataset used for the classification process, the data collected contains unbalanced class results (balance). The unbalanced data classification process becomes an important problem, this is because it can cause a decrease in performance. Adaboost is a technique in data mining that can be used to increase the level of accuracy in classification methods. The results showed that the adaboost algorithm can help improve classification performance. This can be seen from the increasing level of accuracy obtained from the process carried out before and after using the adaboost algorithm. The results obtained from the research show that the adaboost algorithm can be used properly to help the performance of the K-Nearest Neighbor algorithm for the classification process on diabetes datasets. It can be seen from 5 tests with values of K = 7, 13, 19, 25 and 31 there is an increase in the accuracy results obtained after using the adaboost algorithm.

In this paper, the application of data mining techniques on polynomial based approach for better electrocardiogram (ECG) authentication mechanism is presented. Polynomials being used for ECG data processing have a history of nearly two decades. Recently it has been bringing about promising solutions for heart beat recognition problem. General polynomial based approach are used in this research and by using the polynomial coefficients extracted as unique features from the ECG signals, data mining techniques was applied for person identification. A total of 18 ECG recordings from MIT/BIH Normal Sinus Rhythm database (NSRDB) were used for development and evaluation. QRS complexes from each dataset was divided into two parts, the training and the testing dataset which was used to prove the validity of the data mining technique applied. Experimental results was classified using Multilayer Perceptron (MLP) in order to confirm the identity of an individual and was compared with the previous research using polynomials without the use of data mining technique. Our experimentation on a public ECG database suggest that the proposed data mining technique on polynomial based approach significantly improves the identification accuracy by 96% as compared to 87% from the existing study.

Drought has an impact on many aspects of society. To help decision makers reduce the impacts of drought, it is important to improve our understanding of the characteristics and relationships of atmospheric and oceanic parameters that cause drought. In this study, the use of data mining techniques is introduced to find associations between drought and several oceanic and climatic indices that could help users in making knowledgeable decisions about drought responses before the drought actually occurs. Data mining techniques enable users to search for hidden patterns and find association rules for target data sets such as drought episodes. These techniques have been used for commercial applications, medical research, and telecommunications, but not for drought. In this study, two time-series data mining algorithms are used in Nebraska to illustrate the identification of the relationships between oceanic parameters and drought indices. The algorithms provide flexibility in time-series analyses and identify drought episodes separate from normal and wet conditions, and find relationships between drought and oceanic indices in a manner different from the traditional statistical associations. The drought episodes were determined based on the Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI). Associations were observed between drought episodes and oceanic and atmospheric indices that include the Southern Oscillation Index (SOI), the Multivariate ENSO Index (MEI), the Pacific/North American (PNA) index, the North Atlantic Oscillation (NAO) Index, and the Pacific Decadal Oscillation (PDO) Index. The experimental results showed that among these indices, the SOI, MEI, and PDO have relatively stronger relationships with drought episodes over selected stations in Nebraska. Moreover, the study suggests that data mining techniques can help us to monitor drought using oceanic indices as a precursor of drought.

The significance of this research lies in the application of data mining techniques to analyze badminton competitions, allowing for the identification of players’ tactics, strategies, and weaknesses. By uncovering these insights, targeted training and improvement plans can be developed, enhancing player performance and optimizing competitive outcomes. Association rules in data mining techniques are used, and the Apriori algorithm in association rules is analyzed and improved. The ACARMI algorithm (an improved algorithm of constrained association rule mining based on item) is then applied. Then, data from six top badminton competitions is collected and converted into Boolean data suitable for this algorithm. A data mining module is designed to mine the tactical association rules of all players, and a specific analysis is conducted on one of the players, successfully analyzing their tactical strategies. Finally, to verify the effectiveness of the algorithm proposed in this article, FP-Growth algorithm and DHP algorithm are applied to comparative experiments. The experiment shows that compared with the FP-Growth algorithm, the initial and final running times of the algorithm in this article with minimum support and different constraint numbers are around “355 ms and 75 ms,” “150 ms and 45 ms,” respectively; similarly, compared with the DHP algorithm, they are around “165 ms and 35 ms,” “175 ms and 55 ms,” respectively. Under two sets of experiments, the algorithm proposed in this article presents better operational efficiency. This article demonstrates that the use of data mining techniques can provide effective tactical optimization, opponent research, real-time decision support, and long-term development planning assistance for athletes and coaches.

Purpose: The aim of this study is to examine the pattern of women-owned businesses particularly whether country, age, education, citizenship, social class, income, and religion are associated with female entrepreneurship. Theoretical Framework: The study develops a global demographic model of female entrepreneurship using data mining techniques. Design/Methodology/Approach: The study employs a data mining approach on the World Values Survey Wave 7 2017-2020 data that includes 19575 working women in 56 countries. Findings: The results show that the backgrounds of female entrepreneurs are largely heterogenous from one country to another. The most significant factor that influences entrepreneurship is education level while the least is citizenship status. Research, Practical & Social Implications: The study contributes to model a specific set of attributes of female entrepreneurs for each country. Governments could establish appropriate policies to encourage female entrepreneurship that fits with the local environment and is unique to each country. Originality/Value: The study contributes to the use of data mining techniques for producing more comprehensive findings than statistical approaches that are commonly employed in business economics research.

Computer-based technologies have changed our daily lives, such as the way we live, interact, learn, and play. Data collected through these technologies are now being used to support the second round of revolution in all of these areas. Data mining (DM) research has progressed rapidly in recent years, and the information design of educational management systems has also progressed significantly. Using big data technologies, several educational management systems have collected a great deal of information. However, the DM implementation in educational management systems is still in its initial stages. The use of DM techniques in educational management systems is theoretically important and practically useful. With the advancement of information technology (IT) in the field of college education, the present ideological and political theory courses in colleges and universities have gradually developed into information-based teaching, which has increased teaching efficiency and quality. This paper investigates the “work assessment quantitative table” of ideological and political education (IAPE) administration in universities using data mining and clustering technology techniques. In addition, this study utilizes the k-means clustering algorithms to analyze the data from the “work assessment quantitative table” of IAPE of university administration, which successfully overcomes the limitations and inadequacies of standard analysis approaches.

The aim of this study is to highlight the importance of the unsupervised learning in Data mining and CRM fields. Data mining commonly known by its acronym KDD: knowledge discovery in data base, it refers to all methods and algorithms used for data exploration or prediction in large data bases volumes, Data mining is very important in various fields such as science, business and other areas deal with a large data set. CRM: Customer Relationship Management is an integrated information system that is used to plan, schedule and control the pre-sales and post-sales activities in an organization, both CRM and data mining techniques helps organizations maximize the value of every customer interaction and drive superior corporate performance. Clustering is one of the favoured used methods in data mining: The objective of this study is to implement the clustering algorithm K-Medoids via a shell script applied on a set of Analytical CRM data stored in Teradata environment.